Science, Engineering and Technology Proceedings
An international, peer-reviewed open-access platform dedicated to publishing high-quality conference proceedings that explore the intersections of science, engineering, and technology, and their transformative impact on societal advancement. The series aims to provide a comprehensive forum for researchers, scholars, and practitioners to share their latest findings, foster interdisciplinary collaboration, and address global challenges. Each submission undergoes a rigorous peer-review process to ensure the publication of relevant, impactful, and timely content that contributes to advancing scientific knowledge, engineering practices, and technological innovations for the betterment of society.
Research on Deep Learning-Based Intelligent Prediction Models for Quality Deterioration in Grain and Oil Storage
Stored grain and oil quality are linked to a nation's food security and economic benefits. Traditional grain and oil monitoring methods are mostly performed by practitioners using their experience which can lead to poor timeliness and accuracy of information. Therefore, the purpose of this research is to use deep learning technology to build an intelligent predictive model which helps to overcome the issue of maintaining stored grain and oil quality in complicated storage conditions. The model uses multiple types of monitoring data such as temperature, humidity, and pictures to create an advanced quality deterioration early warning system that can support many different practical storage scenarios. Findings from this research indicate that the model can successfully analyze the complex nonlinear relationships among many of the critical components that affect quality. The findings provide a new technical approach for the intelligent and proactive management of grain oil storage quality.
Copyright (c) 2025 Siyi Li (Author) https://creativecommons.org/licenses/by/4.0
A Review of Aircraft Aerodynamic and Structural Optimization Method Based on Artificial Intelligence
With aviation technology continuous development, aircraft design is going toward faster, lighter and more intelligent direction. Aerodynamic shape characteristic and structural stiffness are two very important factor that show aircraft overall performance, so optimize aerodynamic shape and structural stiffness always is hot research direction in aviation system engineering field. Traditional aerodynamic-structure optimization design method usually need consume many CFD and FEA large numerical simulation, and need bigger design freedom and higher design efficiency, hard to fit modern complex aviation system design process. These years, machine learning, deep learning, reinforcement learning and evolutionary algorithm etc. artificial intelligence technology develop very rapid, bring completely new idea for aircraft optimization design, can on data-driven base, through machine learning, deep learning, reinforcement learning and evolutionary algorithm etc. way complete aircraft aerodynamic characteristic prediction, aircraft structural response modeling, based on aircraft aerodynamic and structure multi-objective optimization model global optimization etc. task, thus can effective reduce aircraft design high calculation cost and improve design accuracy. This paper review machine learning, deep learning, reinforcement learning and evolutionary algorithm etc. artificial intelligence technology in aviation aircraft aerodynamic and structural optimization design development and future use technical characteristic, key introduce surrogate model building, through deep neural network do aircraft flow field prediction and shape reconstruction, based on reinforcement learning adaptive control and multi-objective optimization, evolutionary algorithm in aircraft lightweight design application etc. research method, discuss AI technology in aviation aircraft aerodynamic-structure integrated optimization design and multidisciplinary collaborative design application prospect and challenge problem, like data limited, model transparency not enough and multi-scale effect coupling problem etc. At last, put forward artificial intelligence apply to aircraft design further development direction, like physics guide neural network, data scarce learning, cloud intelligent calculation and base on artificial intelligence automation design platform, this paper give research summary and outlook for artificial intelligence in aviation field design application.
Copyright (c) 2025 Haoran Tang (Author) https://creativecommons.org/licenses/by/4.0
New Generation Drug Delivery Systems: Nanocarriers, Smart Hydrogels, and Organelle Targeting Technologies
The field of drug delivery has undergone significant advancements with the emergence of novel technologies such as nanocarriers, smart hydrogels, and organelle-targeting systems. These innovations enable more precise, efficient, and controlled delivery of therapeutic agents, enhancing drug efficacy while minimizing side effects. Nanocarriers, including lipid nanoparticles and polymeric nanoparticles, improve drug solubility and stability, allowing for controlled release and targeted delivery to specific tissues or cells. Smart hydrogels, responsive to environmental stimuli like pH and temperature, offer sustained drug release and site-specific targeting, making them ideal for chronic disease treatment. Organelle-targeting technology, which directs drugs to cellular organelles such as mitochondria and lysosomes, has emerged as a cutting-edge approach to precision medicine. These technologies hold the potential to address challenges like poor drug solubility, non-specific distribution, and treatment resistance. However, challenges remain, including biocompatibility, long-term stability, and scalability. Future research will focus on overcoming these hurdles and integrating these technologies to create personalized and efficient drug delivery systems. The development of multi-functional, hybrid systems combining nanocarriers, hydrogels, and organelle targeting will pave the way for more effective, targeted therapies.
Copyright (c) 2025 Zhichao Wang (Author) https://creativecommons.org/licenses/by/4.0
Analyzing Protein Structures and Functions in Tube A & B from Borduria
During a routine retrieval operation at a remote research station, a field technician collected two unidentified laboratory sample tubes, labeled A and B, for subsequent scientific evaluation. These samples were believed to originate from an ongoing experimental project whose details had not been fully documented, and their analysis was expected to clarify the project’s technical focus and research direction. While the technician was returning with the materials, Tube B was accidentally damaged, resulting in the loss of part of its contents and a brief exposure incident. The technician later developed acute symptoms and was unable to provide further information about the circumstances surrounding the spill. This event increased the urgency of examining the remaining Tube A to determine the nature, stability, and potential laboratory relevance of the preserved sample. The incident also underscored the importance of traceable sample management, standardized handling procedures, and careful cross-disciplinary analysis when dealing with unknown research materials. The following study provides a systematic evaluation of the specimen in Tube A, aiming to identify structural features, possible biological origins, and any functional properties that may be relevant to ongoing scientific work.
cross-disciplinary laboratory evaluation
Copyright (c) 2025 Xinjue Zhou (Author) https://creativecommons.org/licenses/by/4.0
Multi-Agent Reinforcement Learning for Autonomous Driving in Signal-Free Intersections and Roundabouts
Signal-Free Intersections & Roundabouts: One of the most difficult problems that a self-driving car faces is the proper functioning at intersections with no traffic lights. It is because there are no traffic lights, which assist us in making quick decisions, unlike just using lidar sensors and cameras for fixed-lane driving. In these situations the current research on multi-agent reinforcement learning (MARL) does not extent to such domains because of issues like non-stationarity and partial observability. The novel hierarchical learning framework for related communications integrates hierarchical learning structures and complex cost functions, which can better adjust a more adaptive response to safety and traffic flow efficiency.
Impact of Microbial Fermentation Engineering on Food Nutrition and Health-Promoting Properties
Microbial fermentation engineering plays a critical role in enhancing the nutritional quality and functional properties of foods. This review summarizes recent advances in fermentation technologies and their impacts on macronutrients, micronutrients, and anti-nutritional factors, highlighting improvements in protein digestibility, amino acid composition, lipid profiles, vitamin content, and mineral bioavailability. Additionally, fermentation promotes the production of bioactive compounds, including peptides, polyphenols, exopolysaccharides, and gamma-aminobutyric acid (GABA), which contribute to antioxidative, anti-inflammatory, and metabolic health benefits. Probiotic microorganisms in fermented foods support gut health, immune modulation, and metabolic regulation, while fermentation also enhances food safety by reducing mycotoxins, biogenic amines, and pathogenic microbes. The review further discusses current challenges, including variability in microbial metabolism, standardization issues, and regulatory considerations, and highlights the potential of synthetic biology, multi-omics, and predictive fermentation strategies for next-generation functional foods. Overall, microbial fermentation engineering offers a versatile and sustainable approach to improving human nutrition and health, bridging traditional practices with modern biotechnological innovations.
Dissecting the Mechanisms of Neuromuscular Junction Plasticity through a Single-Cell Multi-Omics Lens
The neuromuscular junction (NMJ) serves as the fundamental interface between motor neurons and skeletal muscles, enabling precise motor control. Its structural and functional plasticity underlies muscle regeneration, motor learning, and recovery from injury. Traditional approaches relying on histology and bulk omics have provided valuable insights into NMJ structure but fail to capture cell-type heterogeneity, temporal dynamics, and spatial coordination during regeneration. A systems-level understanding of NMJ plasticity remains limited. This study integrates single-cell RNA sequencing, proteomic, and spatial transcriptomic data to construct a multidimensional framework for NMJ plasticity. Analytical methods include clustering, trajectory inference, ligand-receptor network modeling, and cross-omic module identification, interpreted under the Neuro-Muscular Plasticity Systems Framework (NMPSF). The analysis reveals specialized Schwann cell subtypes, bidirectional neuron-muscle communication through neuregulin-ErbB and agrin-MuSK pathways, and spatially organized extracellular matrix remodeling supporting reinnervation. Integrated multi-omic networks further identify molecular modules governing synaptic stability, regeneration, and immune-ECM interaction. These findings redefine NMJ plasticity as a coordinated, multi-level process linking molecular signaling, cellular diversity, and spatial organization. The proposed NMPSF offers a reproducible analytical and conceptual framework for studying neuromuscular adaptation and guiding regenerative interventions for neuromuscular disorders.
Copyright (c) 2025 Mingyang Gao (Author) https://creativecommons.org/licenses/by/4.0
Terahertz Perfect Absorption Based on Ultrathin Conductive Films
Electromagnetic absorption is one of the most fundamental forms of light-matter interaction, spanning a broad spectral range from radio frequency to the ultraviolet band. In the terahertz (THz) regime, however, conventional materials typically exhibit weak absorption, which limits the performance of high-efficiency THz devices such as detectors, modulators, and sensors. The development of ultrathin conductive films (UTCFs), especially two-dimensional materials, offers new opportunities for enhancing THz absorption due to their unique optical and electrical characteristics. Yet, the extremely small thickness of these UTCFs often restricts their ability to sustain strong interaction with incident THz waves, making additional strategies necessary to achieve substantial absorption. In this work, using graphene as a representative UTCF, we demonstrate that near-perfect THz absorption can be realized through a total internal reflection (TIR) configuration. When the refractive index of the superstrate exceeds that of the substrate, TIR occurs once the incident angle surpasses the critical angle, enabling the confined evanescent field to interact strongly with the ultrathin film. This mechanism leads to highly efficient THz absorption in UTCF-based structures and supports a broadband and wide-angle near-perfect absorption region around the optimal absorption point. A detailed parameter-dependence analysis is conducted for both the UTCF and the surrounding dielectric environment, illustrating how the absorption peak evolves with material conductivity, film thickness, refractive-index contrast, and incidence angle. The findings provide a refined physical understanding and a practical design pathway for developing strong THz absorbers based on ultrathin conductive materials, contributing to future applications in THz sensing, imaging, and integrated photonic systems.
Copyright (c) 2025 KeLiang Luo (Author) https://creativecommons.org/licenses/by/4.0
Contemporary Research on Interdisciplinary Professional Development Methods
Contemporary professional development increasingly demands interdisciplinary approaches that transcend traditional sectoral boundaries. This paper examines emerging methods and frameworks for enhancing professional competencies across diverse fields, with particular emphasis on technology-driven efficiency, project management innovation, and post-pandemic professional adaptation. The research investigates how digital transformation initiatives, collaborative learning environments, and competency-based training models contribute to workforce advancement in rapidly evolving industries. Through systematic analysis of contemporary practices, this study identifies critical success factors including technological integration, cross-functional collaboration, and adaptive learning strategies. The findings reveal that effective professional development programs incorporate blended learning modalities, real-time performance analytics, and industry-specific customization while maintaining transferable core competencies. This research contributes to understanding how organizations can design and implement sustainable professional development systems that respond to changing market demands, technological disruptions, and evolving workforce expectations. The implications extend to policy formulation, organizational strategy, and educational curriculum design for professional training programs.
Copyright (c) 2025 Ming Gao, Jie Huang, Rui Qian (Author) https://creativecommons.org/licenses/by/4.0
Application of Chemical Stabilizers to Sustain Urease Inhibition in Soil–Plant Systems
Nitrogen fertilizers play a crucial role in enhancing crop yield and quality in agricultural production. However, urea, as a major nitrogen source, is rapidly hydrolyzed by urease in soil, leading to nitrogen loss through ammonia volatilization or nitrate leaching. This not only reduces fertilizer use efficiency but also increases environmental pollution. Urease inhibitors can delay urea decomposition and improve nitrogen utilization, but their activity in soil is often rapidly diminished due to temperature, moisture, and microbial activity, limiting their long-term application. The introduction of chemical stabilizers provides a new strategy to prolong the effective period of urease inhibitors. In this study, urea and urease inhibitors were applied in a simulated soil–plant system, with the addition of different types of chemical stabilizers. The effects on urease activity, soil nitrogen dynamics, and plant growth and nitrogen uptake were systematically evaluated. The results showed that chemical stabilizers significantly slowed the degradation of urease inhibitors, maintaining low soil urease activity for an extended period. Correspondingly, ammonia volatilization was markedly reduced, and nitrate retention increased, indicating effective control of soil nitrogen loss. Meanwhile, plant nitrogen uptake and growth performance were improved, demonstrating that stabilizers can not only prolong the inhibitory effect but also promote crop growth. Mechanistic analysis suggested that chemical stabilizers may extend inhibitor activity by forming protective complexes with the inhibitors or by reducing microbial degradation rates. This strategy has significant potential for agricultural applications, as it can improve nitrogen use efficiency, reduce environmental pollution, and provide a theoretical basis for optimizing soil–plant nitrogen management. Future studies could further explore the effectiveness and applicability under different soil types, crop species, and long-term field conditions.
Copyright (c) 2025 Alexander Carter (Author) https://creativecommons.org/licenses/by/4.0
Metal-Catalyzed Reactions for Energy and Biomedical Applications
Metal-catalyzed reactions have emerged as cornerstone technologies bridging energy conversion systems and biomedical therapeutics through their remarkable selectivity, efficiency, and tunability. This review examines the dual convergence of metal catalysis in addressing critical challenges in sustainable energy production and advanced healthcare delivery. The discussion encompasses single-atom catalysts, dual-metal site architectures, and coordination polymer frameworks that enable transformative processes including electrocatalytic carbon dioxide reduction, fuel cell oxygen reduction, hydrogen evolution, and targeted therapeutic interventions. Recent advances in biohybrid catalysis and metal-organic frameworks have demonstrated unprecedented control over reaction pathways and biological interactions, creating opportunities for integrated solutions to energy sustainability and human health challenges. Through analysis of catalyst design principles, mechanistic understanding, and structure-activity relationships, this work illustrates how strategic manipulation of metal centers, ligand environments, and support materials drives performance optimization across both domains. The synthesis of knowledge from energy catalysis and catalytic biomedicine reveals common design strategies and emerging opportunities for cross-disciplinary innovation that will shape future development of multifunctional metal-based systems capable of addressing interconnected global challenges in climate, energy security, and healthcare accessibility.
Copyright (c) 2025 Jessica Lin, Michae Chen (Author) https://creativecommons.org/licenses/by/4.0
Modern Management Approaches for Complex Interdisciplinary Systems
Complex interdisciplinary systems present unprecedented management challenges requiring innovative approaches that transcend traditional project management paradigms. This paper examines contemporary management methodologies applicable across diverse domains including project evaluation frameworks, leadership practices, and performance optimization strategies. The investigation reveals that effective management of complex systems demands multidimensional evaluation criteria, adaptive leadership competencies, and integration of digital technologies with human expertise. Through systematic analysis of management practices in construction, software development, manufacturing, and professional training contexts, this study identifies convergent principles applicable across disciplinary boundaries. The findings demonstrate that successful management of interdisciplinary systems requires balance between standardized processes and contextual adaptation, emphasis on stakeholder engagement alongside technical excellence, and integration of quantitative metrics with qualitative assessment. Comparative analyses of leadership behaviors, project success factors, and performance evaluation methodologies reveal that contemporary management approaches prioritize flexibility, continuous learning, and collaborative problem-solving over rigid adherence to predetermined plans. These insights contribute to understanding how management frameworks can effectively address complexity, uncertainty, and rapid change characterizing modern interdisciplinary systems across professional domains.
Copyright (c) 2025 Yang Liu, Wei Zhang (Author) https://creativecommons.org/licenses/by/4.0
Data Analytics Applications in Diverse Professional Domains
Data analytics has emerged as a transformative force across diverse professional domains, fundamentally altering decision-making processes and operational methodologies. This paper examines the application of analytical frameworks in multiple sectors including business intelligence, manufacturing systems, construction management, and digital platforms. The investigation reveals that analytics-driven approaches enable organizations to extract actionable insights from complex datasets, optimize performance metrics, and enhance strategic planning capabilities. Through systematic analysis of implementation strategies across various professional contexts, this study identifies common challenges including data quality issues, analytical skill gaps, and organizational resistance to data-driven paradigms. The findings demonstrate that successful analytics adoption requires integration of technological infrastructure with human expertise and organizational culture transformation. Comparative analyses of analytics applications in domains such as supply chain optimization, user engagement enhancement, risk assessment, and performance evaluation reveal convergent methodologies that transcend disciplinary boundaries. These insights contribute to understanding how data-driven decision-making frameworks can be adapted and scaled across different professional environments, providing practical guidance for organizations seeking to leverage analytics capabilities for competitive advantage and operational excellence.
Copyright (c) 2025 Priya Singh, Daniel Wang, Marie Dubois (Author) https://creativecommons.org/licenses/by/4.0
Functional Coordination Polymers with Dual Catalytic Applications
Functional coordination polymers have emerged as remarkable materials exhibiting dual catalytic capabilities that enable simultaneous or sequential transformations in complex reaction systems. These crystalline porous materials, constructed from metal ions or clusters connected by organic linkers, possess unique structural features including high surface areas, tunable pore environments, and diverse metal coordination sites that facilitate multiple catalytic functions. The integration of different catalytic functionalities within a single framework enables cascade reactions, tandem transformations, and multifunctional catalytic processes that would be difficult to achieve using conventional catalysts. This review examines the design principles underlying dual-functional coordination polymers, emphasizing how structural architecture, metal center selection, and ligand design contribute to multifunctional catalytic behavior. The synthetic strategies for creating coordination polymers with controlled dual functionalities are discussed, along with characterization approaches that reveal structure-activity relationships. Applications in various catalytic processes including organic transformations, environmental remediation, electrochemical reactions, and biomimetic catalysis are comprehensively analyzed. Special attention is given to systems demonstrating synergistic effects where dual functionalities enhance overall catalytic performance beyond simple additive contributions. The challenges associated with maintaining structural stability during catalytic processes and strategies for optimizing dual catalytic activities are addressed. This comprehensive analysis demonstrates that functional coordination polymers represent a powerful platform for developing advanced catalytic systems with multiple integrated functionalities.
Copyright (c) 2025 Ming Patel, Sofia Zhang, Hiroshi Tanaka (Author) https://creativecommons.org/licenses/by/4.0
Multi-Metal Catalytic Systems for Selective Product Formation
Multi-metal catalytic systems have emerged as powerful platforms for achieving selective product formation in various chemical transformations. These systems leverage synergistic interactions between different metal centers to enhance catalytic activity, improve selectivity, and enable reactions that are challenging for single-metal catalysts. The strategic combination of metals creates unique electronic and geometric properties that facilitate targeted reaction pathways while suppressing undesired side reactions. This review examines the fundamental principles governing multi-metal catalysis, including electronic effects, geometric modifications, and cooperative mechanisms between metal sites. The application of bimetallic and trimetallic catalysts in hydrogenation reactions, carbon dioxide reduction, biomass conversion, and other industrially relevant processes is comprehensively discussed. Special attention is given to the design strategies that enable precise control over product distribution, including support selection, metal composition optimization, and surface engineering approaches. Recent advances in characterization techniques have provided deeper insights into the structure-activity relationships in multi-metal systems, facilitating rational catalyst design. The challenges associated with maintaining selectivity under various reaction conditions and strategies for catalyst stability enhancement are also addressed. This comprehensive analysis demonstrates that multi-metal catalytic systems represent a promising avenue for developing sustainable and efficient chemical processes with enhanced selectivity.
Copyright (c) 2025 David Chen, Yuki Nakamura (Author) https://creativecommons.org/licenses/by/4.0
Structural Engineering of Metal Sites for Catalysis
Structural engineering of metal sites has emerged as a pivotal strategy for advancing heterogeneous catalysis, enabling unprecedented control over catalytic activity, selectivity, and stability. The precise manipulation of metal coordination environments, metal-support interactions, and spatial arrangements of active sites provides opportunities to tailor catalytic properties for specific reactions. Single-atom catalysts represent a frontier in this field, offering maximum atomic efficiency and unique electronic structures that differ fundamentally from nanoparticles and bulk materials. The coordination geometry, oxidation state, and local environment of isolated metal atoms critically determine their catalytic performance in reactions ranging from electrocatalytic carbon dioxide reduction to hydrogen peroxide synthesis. Dual-metal site architectures introduce additional complexity and synergy, enabling tandem catalytic processes and cooperative substrate activation. This paper examines the principles and strategies of structural engineering for metal catalytic sites, exploring the relationships between atomic-scale structure and catalytic function. Recent advances in synthesis methods, characterization techniques, and theoretical understanding have enabled rational design of metal sites with optimized properties. The integration of experimental observations with computational predictions continues to drive progress in developing next-generation catalysts for energy conversion, environmental remediation, and chemical synthesis applications.
Copyright (c) 2025 Jian Liu, Anna Rodriguez, Kenji Yamamoto (Author) https://creativecommons.org/licenses/by/4.0
Electrocatalytic and Enzymatic Inhibition by Metal Complexes
Metal complexes have emerged as versatile compounds with significant applications in both electrocatalysis and enzymatic inhibition. The unique electronic properties and coordination geometries of transition metal complexes enable them to facilitate electron transfer processes in electrocatalytic reactions while simultaneously interacting with biological enzymes to modulate their activity. This paper explores the dual functionality of metal complexes in advancing electrocatalytic processes for energy conversion and environmental remediation, as well as their therapeutic potential through enzyme inhibition mechanisms. The coordination environment, oxidation states, and ligand architectures play crucial roles in determining the efficacy of metal complexes in both domains. Recent developments in copper-based coordination polymers, ruthenium catalysts, and zinc-containing metalloenzyme inhibitors demonstrate the growing importance of rational design strategies. Understanding the structure-activity relationships and mechanistic pathways is essential for developing next-generation catalysts and therapeutic agents. This review examines the current state of metal complex applications, highlighting key design principles, mechanistic insights, and future directions for research in this interdisciplinary field spanning materials chemistry, bioinorganic chemistry, and medicinal chemistry.
Copyright (c) 2025 Sarah Kim (Author) https://creativecommons.org/licenses/by/4.0
Enzyme Inhibitor Development Using Metal-Organic Framework Technology: Therapeutic and Agricultural Potential
Metal-organic frameworks (MOFs) have emerged as revolutionary platforms for enzyme inhibitor development, offering unprecedented opportunities in both therapeutic and agricultural applications. These crystalline porous materials, composed of metal nodes connected by organic linkers, provide unique structural properties that enable precise control over enzyme-inhibitor interactions. The high surface area, tunable pore sizes, and customizable chemical environments of MOFs facilitate the design of highly selective and efficient enzyme inhibitors. In therapeutic applications, MOF-based enzyme inhibitors show remarkable potential for treating various diseases by targeting specific enzymatic pathways while minimizing off-target effects. Agricultural applications demonstrate significant promise in developing environmentally sustainable enzyme inhibitors for pest control and crop protection. The versatility of MOF platforms allows for the incorporation of multiple inhibitory mechanisms within a single framework, enhancing overall efficacy. Recent advances in MOF synthesis and functionalization have enabled the development of stimuli-responsive inhibitor systems that can be activated under specific conditions. This comprehensive review examines the current state of MOF-based enzyme inhibitor development, highlighting key breakthroughs in design strategies, synthesis methodologies, and practical applications. The integration of computational modeling with experimental validation has accelerated the discovery of novel inhibitor-MOF combinations with enhanced selectivity and potency.
Copyright (c) 2025 Liang Chen (Author) https://creativecommons.org/licenses/by/4.0
Urease Inhibition Mechanisms in Soil-Plant Systems Using Coordination Polymers for Sustainable Agriculture
The increasing demand for sustainable agricultural practices has intensified research into enzyme inhibition mechanisms that can optimize nitrogen utilization efficiency in soil-plant systems. Urease, a critical enzyme responsible for urea hydrolysis in soils, plays a pivotal role in nitrogen cycling but often leads to significant nitrogen losses through ammonia volatilization when not properly regulated. This study investigates the application of coordination polymers as innovative urease inhibitors in soil-plant systems, focusing on their mechanisms of action, environmental stability, and agricultural implications. Coordination polymers, characterized by their unique structural properties and tunable chemical compositions, offer promising solutions for controlled enzyme inhibition while maintaining soil health and supporting plant growth. The research examines various copper-based coordination polymers and their effectiveness in prolonging urease inhibition compared to conventional chemical stabilizers. Results demonstrate that coordination polymers exhibit superior performance in maintaining enzyme inhibition over extended periods, with minimal adverse effects on beneficial soil microorganisms and plant development. The study also evaluates the impact of these inhibitors on soil carbon, nitrogen, and phosphorus dynamics, revealing enhanced nutrient retention and improved fertilizer use efficiency. Furthermore, the investigation explores the relationship between coordination polymer structure and inhibition selectivity, providing insights into the design of next-generation agricultural amendments. These findings contribute to the development of environmentally sustainable approaches to nitrogen management in agricultural systems, offering potential solutions to reduce greenhouse gas emissions while maintaining crop productivity.
Copyright (c) 2025 James Hu (Author) https://creativecommons.org/licenses/by/4.0
Design of Metal–Organic Frameworks with V-Shaped Auxiliary Ligands: Insights into Structure and Function
Metal-organic frameworks (MOFs) represent a revolutionary class of crystalline materials that have garnered significant attention due to their exceptional structural diversity and multifunctional properties. The incorporation of V-shaped auxiliary ligands in MOF design has emerged as a powerful strategy for controlling structural architecture and enhancing functional capabilities. This comprehensive review examines the role of V-shaped ligands in directing the formation of three-dimensional hybrid networks, influencing pore characteristics, and enabling specific applications ranging from gas adsorption to catalysis and drug delivery. The geometric constraints imposed by V-shaped ligands provide unique opportunities for engineering breathing behaviors, flexibility, and selective binding properties. Recent advances demonstrate that strategic use of V-shaped auxiliary ligands significantly improves thermal stability, enhances sorption properties, and creates highly efficient catalytic systems. Furthermore, these ligands enable MOFs with specialized functionalities, including electrochromic properties, supercapacitor applications, and enzyme inhibition capabilities. This review synthesizes current understanding of structure-property relationships in V-shaped ligand-based MOFs, highlighting key design principles and emerging applications.
Copyright (c) 2025 Yang Chen (Author) https://creativecommons.org/licenses/by/4.0
Independent Predictive Factors for Positive Margins after Conization of Cervical HSIL in Women of Childbearing Age and the Necessity of Secondary Surgery
This study aims to analyze the high-risk factors for positive surgical margins after loop electrosurgical excision procedure (LEEP) and the necessity of secondary surgery in women of childbearing age (20-40 years) with high-grade squamous intraepithelial lesions (HSIL). Through retrospective analysis of 526 HSIL patients who underwent LEEP at the Third Affiliated Hospital of Sun Yat-sen University from 2015 to 2019, the positive margin rate was found to be 22.43%. Multivariate analysis indicated that TCT result of HSIL (OR = 3.414, 95%CI = 1.793-6.500, P < 0.001) and lesions involving ≥5 points (OR = 8.567, 95%CI = 4.439-16.533, P < 0.001) were independent risk factors for positive margins. Among 34 patients who underwent secondary surgery within six months after initial conization, 32 had positive margins, and the residual disease rate among them was only 21.88% (7/32), indicating that positive margins do not necessarily lead to residual lesions. Therefore, the decision for secondary surgery in patients with positive margins should be individualized based on factors such as age, fertility needs, lesion characteristics, complications, and personal preferences, rather than reflexively performing secondary surgery.
cervical high-grade squamous intraepithelial lesion
Copyright (c) 2025 Yingying Huang (Author) https://creativecommons.org/licenses/by/4.0
Length-Weight Relationship Modeling Applications in Aquatic Species Biomass Estimation Methods
Length-weight relationships represent fundamental biometric tools in aquatic ecology, providing essential methods for biomass estimation across diverse marine and freshwater species. These allometric relationships enable researchers to convert easily measured morphometric parameters into biomass values, facilitating population assessments and ecosystem monitoring. Contemporary approaches integrate traditional morphometric measurements with advanced technologies including metabarcoding, machine learning, and in situ imaging systems. The application of length-weight models spans multiple taxonomic groups, from zooplankton communities to large crustaceans and fish populations. Recent developments emphasize spatiotemporal modeling approaches that account for environmental variability and seasonal dynamics in aquatic systems. Machine learning algorithms have enhanced biomass estimation accuracy by incorporating multiple morphometric variables beyond simple length measurements. Metabarcoding techniques now enable species-specific biomass calculations from genetic material, revolutionizing plankton community assessments. The integration of these methodologies addresses critical challenges in aquatic biodiversity monitoring and fisheries management. This review synthesizes current applications of length-weight relationship modeling, examining methodological advances in biomass estimation techniques across aquatic ecosystems. The analysis encompasses traditional regression approaches, contemporary technological innovations, and emerging computational methods that improve estimation precision. Understanding these relationships remains crucial for sustainable fisheries management, ecosystem health assessment, and biodiversity conservation in aquatic environments.
Copyright (c) 2025 Omar Siddiqui (Author) https://creativecommons.org/licenses/by/4.0
Migration Infrastructure Design Impacts on Riverine Crustacean Population Dynamics Studies
The proliferation of anthropogenic barriers in riverine systems has fundamentally altered the population dynamics of aquatic organisms, particularly migratory crustaceans. This paper examines how migration infrastructure design influences crustacean population dynamics through comprehensive analysis of barrier effects, passageway effectiveness, and population fragmentation patterns. River fragmentation disrupts natural migration patterns essential for crustacean life cycles, leading to population isolation and demographic changes. The construction of migration passageways represents a critical intervention strategy, yet their design parameters significantly influence success rates. This study synthesizes current understanding of barrier impacts on crustacean populations, evaluating infrastructure design principles that promote population connectivity. Key findings indicate that passageway design features including gradient, substrate composition, and flow characteristics directly influence migration success. Population dynamics modeling reveals that even partial connectivity restoration can substantially improve population stability in fragmented systems. The research highlights the importance of species-specific design considerations, particularly for commercially important species. Furthermore, the temporal aspects of migration infrastructure operation significantly affect population recruitment patterns. This comprehensive analysis provides essential guidance for designing effective migration infrastructure that supports sustainable crustacean populations while accommodating human development needs. The findings emphasize the critical role of adaptive management approaches in optimizing infrastructure performance for long-term population viability.
Copyright (c) 2025 Liang Ming (Author) https://creativecommons.org/licenses/by/4.0
Carapace Measurement Techniques for Determining Growth Rates in Cultured Crustaceans
Accurate measurement of carapace dimensions is fundamental for assessing growth rates in cultured crustaceans, providing essential data for aquaculture management and biological research. This paper examines various carapace measurement methodologies employed in crustacean cultivation, analyzing their precision, applicability, and effectiveness across different species. Traditional morphometric approaches, including direct caliper measurements and photographic analysis, are evaluated alongside modern digital imaging techniques and automated measurement systems. The study reviews measurement protocols for major cultured species including mud crabs, red king crabs, lobsters, and freshwater crayfish, examining how environmental factors such as temperature influence growth patterns and measurement accuracy. Standardized measurement techniques are critical for establishing reliable length-weight relationships and determining size at maturity parameters essential for aquaculture optimization. The analysis reveals that carapace width measurements generally provide more consistent growth indicators than length measurements, particularly in brachyuran species. Temperature-dependent growth responses necessitate controlled measurement environments and standardized protocols to ensure data comparability across studies. Integration of morphometric data with molting frequency observations enhances growth rate calculations and provides comprehensive assessment tools for aquaculture practitioners. Recommendations include adoption of standardized measurement protocols, implementation of digital imaging systems for improved accuracy, and establishment of species-specific measurement guidelines to optimize growth monitoring in commercial crustacean culture operations.
Copyright (c) 2025 Ravi Mahadevan (Author) https://creativecommons.org/licenses/by/4.0
Freshwater Ecosystem Management Strategies for Commercially Important Decapod Species Populations
Freshwater decapod crustaceans represent economically vital components of aquatic ecosystems worldwide, yet their populations face unprecedented threats from anthropogenic pressures including habitat degradation, pollution, and climate change. This paper examines comprehensive management strategies for commercially important freshwater decapod species, with particular emphasis on sustainable conservation approaches that balance ecological integrity with economic demands. The analysis encompasses ecosystem-based management frameworks, species-specific conservation protocols, and integrated multitrophic aquaculture systems that support both wild population recovery and commercial viability. Through examination of successful case studies from major river systems including the Yangtze River and Lake Victoria Basin, this study demonstrates that effective decapod management requires adaptive strategies incorporating habitat restoration, migration corridor establishment, water quality management, and community-based conservation initiatives. The research reveals that ecosystem-based approaches significantly outperform single-species management strategies in maintaining population stability and genetic diversity. Key findings indicate that migration passageway construction, nutrient cycling optimization, and stakeholder engagement represent critical components of successful conservation programs. The paper concludes that sustainable freshwater decapod management necessitates integrated approaches combining scientific research, traditional ecological knowledge, and adaptive management frameworks to address the complex challenges facing these commercially valuable species in rapidly changing aquatic environments.
Copyright (c) 2025 Nguyen Thi Lan (Author) https://creativecommons.org/licenses/by/4.0
Crustacean Morphological Adaptations in Controlled Aquaculture Environments and Feeding Systems
Crustacean aquaculture has experienced unprecedented growth in recent decades, necessitating comprehensive understanding of morphological adaptations in controlled environments. This study examines the physiological and morphological responses of commercially important crustacean species to various aquaculture conditions, with particular emphasis on feeding systems, environmental stressors, and immune responses. The research reveals significant morphological plasticity in gill structures, digestive systems, and immune organs when crustaceans are subjected to different aquaculture conditions. Key findings indicate that controlled feeding regimens significantly influence carapace dimensions, antennae development, and hepatopancreas functionality. Environmental parameters such as water quality, stocking density, and nutritional composition directly correlate with morphological adaptations in respiratory systems and immune cell populations. The study demonstrates that crustaceans exhibit remarkable phenotypic flexibility, adapting their gill morphology by up to 35% in response to varying oxygen levels and developing enhanced hepatopancreas functionality under optimized feeding protocols. These adaptations have profound implications for aquaculture productivity, disease resistance, and sustainable production practices. Understanding these morphological responses enables the development of species-specific culture protocols that maximize growth efficiency while maintaining physiological health, ultimately contributing to the advancement of sustainable crustacean aquaculture practices worldwide.
Copyright (c) 2025 Jason McAllister (Author) https://creativecommons.org/licenses/by/4.0
Spatiotemporal Modeling of Soil Moisture in Humid Areas by Integrating Transformer Architecture and Remote Sensing Data
Soil moisture variation in humid regions presents high-frequency nonlinearity and is strongly influenced by surface conditions, making it difficult for traditional time series models to effectively capture long-range dependencies. In this study, a multimodal Transformer network architecture is proposed, integrating Sentinel-1 VV/VH radar data, MODIS NDVI, and meteorological variables to predict daily 0–10 cm soil moisture content at a 1 km² grid scale in Jiangning District, Nanjing. Data from 2021 were used for model training, and 2022 data were used for testing. Verified by in-situ measurements, the monthly average of 0–10 cm soil moisture in Jiangning during 2021 ranged from 0.20 to 0.32 m³/m³, with a standard deviation of 0.035 m³/m³, reflecting its high-frequency nonlinear characteristics. The model achieved an average RMSE of 0.022 m³/m³, which was lower than that of LSTM (RMSE = 0.029) and traditional SVR (RMSE = 0.034). The model's interpretability module (attention map) showed that vegetation cover and rainfall in the previous six days contributed 41.2% and 27.8%, respectively. This study provides an AI-based approach for modeling soil–vegetation–hydrology interactions driven by remote sensing data.
Copyright (c) 2025 Haojun Li, Rebecca S. Miller, Daniel T. Nguyen, Christopher J. Roberts (Author) https://creativecommons.org/licenses/by/4.0
Leveraging NLP for Semantic and Numerical Inconsistency Detection in Tax Submissions
This study presents an innovative method for identifying tax fraud through the application of natural language processing (NLP) to uncover irregularities within tax documents. Departing from conventional approaches that rely primarily on numerical analysis, the proposed framework combines domain-specific BERT embeddings with bidirectional LSTM architectures to effectively capture nuanced contextual information. A hybrid ensemble architecture is developed to process both structured data and free-text components within tax returns, facilitating the identification of semantic associations among financial entities and exposing numerical inconsistencies. The system was evaluated on a dataset comprising 15,000 tax documents, of which 8.5% were identified as fraudulent. The proposed model achieved superior performance, with an F1-score of 0.868 and an AUC of 0.931 — marking a 7.6% enhancement over leading existing models. Detection effectiveness varied by document category: individual income tax filings yielded an F1-score of 0.889, outperforming business-related filings, which scored 0.818. Further examination reveals that semantic features are particularly effective for identifying fraud in corporate tax documents, while numerical coherence indicators are more significant for personal filings. Although the approach requires higher computational resources compared to conventional techniques, its capacity to detect complex fraud schemes — especially those that disguise manipulation within textual content while maintaining plausible numeric data — offers a significant improvement to current tax fraud detection systems.
Copyright (c) 2025 Jiayu Liang, Jiayan Fan, Zhen Feng, Jing Xin (Author) https://creativecommons.org/licenses/by/4.0
A Fault Diagnosis Method for the Charging and Discharging Water System of a Certain Ship Lift Based on Historical Data Analysis
This paper takes the hydraulic action of the discharge pipeline in the filling and discharging water system of a Certain ship lift as a case study. Based on analysis of operational data from different historical stages, a mathematical model is established to determine whether the equipment in the filling and discharging water system shows trends toward abnormal conditions. The proposed model will provide early warnings for equipment maintenance and reduce the frequency of timeout fault alarms for pipeline water extraction.
Copyright (c) 2025 Binbin Zou, Yansong Lei, Gengbang Huang, Lingzhi Yan (Author) https://creativecommons.org/licenses/by/4.0
Study on Whole-Life-Cycle Management of Green Building Projects
With the deepening of global sustainable development concepts, green buildings have demonstrated significant value in energy conservation, emission reduction, efficient resource utilization, and environmental friendliness. However, traditional project management often focuses on a single phase and lacks systematic control spanning from planning and design through operation and decommissioning. Based on Life Cycle Management (LCM) theory, this paper constructs a whole-life-cycle management framework covering four stages — planning and design, construction implementation, operation and maintenance, and decommissioning and recycling — and proposes key methods such as target setting, performance monitoring, cost control, and risk management. Through case studies of representative domestic and international green building projects, the effectiveness of the proposed indicator system and management strategies in controlling energy consumption, reducing carbon emissions, and optimizing economic benefits is demonstrated. The study shows that whole-life-cycle management not only enhances the overall performance of green buildings, but also promotes industry standardization and technological innovation, providing decision-making references and practical guidelines for governments, owners, and design-build teams.
Copyright (c) 2025 Tiantian Jia (Author) https://creativecommons.org/licenses/by/4.0